Probabilistic Predictions of People Perusing: Evaluating Metrics of Language Model Performance for Psycholinguistic Modeling

Yiding Hao; Simon Mendelsohn; Rachel Sterneck; Randi Martinez; Robert Frank

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arXiv · 2009.03954

Probabilistic Predictions of People Perusing: Evaluating Metrics of Language Model Performance for Psycholinguistic Modeling

Yiding Hao·Simon Mendelsohn·Rachel Sterneck·Randi Martinez·Robert Frank

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Abstract

By positing a relationship between naturalistic reading times and information-theoretic surprisal, surprisal theory (Hale, 2001; Levy, 2008) provides a natural interface between language models and psycholinguistic models. This paper re-evaluates a claim due to Goodkind and Bicknell (2018) that a language model's ability to model reading times is a linear function of its perplexity. By extending Goodkind and Bicknell's analysis to modern neural architectures, we show that the proposed relation does not always hold for Long Short-Term Memory networks, Transformers, and pre-trained models. We introduce an alternate measure of language modeling performance called predictability norm correlation based on Cloze probabilities measured from human subjects. Our new metric yields a more robust relationship between language model quality and psycholinguistic modeling performance that allows for comparison between models with different training configurations.

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Yiding Hao, Simon Mendelsohn, Rachel Sterneck, Randi Martinez, Robert Frank. 2020-09-08. Probabilistic Predictions of People Perusing: Evaluating Metrics of Language Model Performance for Psycholinguistic Modeling. https://arxiv.org/abs/2009.03954

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